LAUSR

In this paper, the novel method that infers human emotions connecting to changes in brain waves is investigated. General methods or algorithms in this field of research extract the features from brain waves and use classifiers such as Support Vector Machine (SVM) and K-Near Neighbor (KNN), based on the artificial intelligence to explain human emotions. The novel method presented in this paper is to use a data table instead of involving the artificial intelligence-based classifiers of these common research methods. In order to prove the method proposed in this paper, the sounds of Niagara Falls, which is clearly nature sounds, recorded from near and far places are used as excitation signals to stimulate the brain. A simulation was performed to infer human emotions by analyzing and interpreting the brain waves obtained as a result of this experiment. The results of experiments and simulations performed in this paper fully demonstrate the method of extracting features from the measured EEG signals presented in this paper and inferring human emotions from the data table. Keyword: Human Emotions, Brain Waves, SVM, KNN, Niagara Falls, Extracting Features, EEG